Morphometric distribution of depth and basin shape in Aotearoa New Zealand lakes
Tadhg Moore
Limnotrack
Morphometric distribution of depth and basin shape in Aotearoa New Zealand lakes
Tadhg Moore
Overview
- Background
- Methods
- Results
- Conclusions
- Future Work
Motivation
- Developing the Lake Ecosystem Research New Zealand modelling platfomr (LERNZmp).
- Lake depth and morphometry are key drivers of lake ecosystem function, therefore accurate depth data is essential.
Background
- Lack of a central database on lake morphometry, particularly depth
- Large number of bathymetric data has been recorded across lakes in New Zealand
- Current predictions of max lake depth, within the FENZ database (n=3588) are limited and have clear biases
Max depth vs Lake area
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Figure 2: Depth vs Area of FENZ lakes
Locations of lakes with bathymetry data in New Zealand
Methods
- Bathymetry survey data were sourced from a variety of sources - paper maps, digital files, and online databases (n=.
- Shoreline data were updated using satellite and aerial imagery.
- Depth data were interpolated using a Multilevel B-Spline Approximation (MBA) algorithm.
- Hypsographic curves were calculated for each lake.
- Comparison of depth and area with other databases.
- Used machine learning to predict lake depth from morphometric data for all lakes in New Zealand and benchmark against the FENZ dataset.
Results
Bathymetric data from 156 lakes were digitised and collated for use in this study.
Using a machine learning model, we were able to predict maximum lake depth from morphometric data with an \(r^2\) = 0.8 and mean lake depth with an \(r^2\) = 0.6.
This was substantially better than the FENZ dataset, which had an \(r^2\) = 0.4 for max depth and 0.2 for mean depth.
Predicting mean and max depth
